Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session N01: Variability in Biological and Living Systems IFocus
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Sponsoring Units: GSNP DBIO Chair: William Ludington, Carnegie Institute of Washington Room: Room 124 |
Wednesday, March 8, 2023 11:30AM - 12:06PM |
N01.00001: Lower bounding variability in microbiome acquisition and exponential growth Invited Speaker: Eric Jones Microbiome composition influences host health, but we do not yet fully understand how microbiomes are assembled nor how the vast diversity of microbiome compositions across individuals is maintained. We examine two factors that contribute to variability in microbiome composition: (i) hosts are constantly exposed to microbial species that may or may not colonize their gut, and (ii) intrinsic variability in microbial division timing compounds to produce macroscopic variability in exponentially growing systems. Data from microbiome assembly in fruit flies and growth experiments in E. coli, S. aureus, and L. minor empirically demonstrate that replicates of identically prepared experimental systems are nonetheless variable. We employ theoretical microscopic models that lower bound the variability associated with these processes and show they are consistent with experiments. These findings yield design principles for microbiome-based therapies, while also placing limits on their efficacy. |
Wednesday, March 8, 2023 12:06PM - 12:18PM |
N01.00002: Statistical Theory of Asymmetric Damage Segregation in Clonal Cell Populations Lev S Tsimring, Arkady Pikovsky Asymmetric damage segregation (ADS) is ubiquitous among unicellular organisms: After a mother cell divides, its two daughter cells receive sometimes slightly, sometimes strongly different fractions of damaged proteins accumulated in the mother cell. Previous studies demonstrated that ADS provides a selective advantage over symmetrically dividing cells by rejuvenating and perpetuating the population as a whole. In this work we focus on the statistical properties of damage in individual lineages and the overall damage distributions in growing populations for a variety of ADS models with different rules governing damage accumulation, segregation, and the lifetime dependence on damage. We show that for a large class of deterministic ADS rules the trajectories of damage along the lineages are chaotic, and the distributions of damage in cells born at a given time asymptotically becomes fractal. By exploiting the analogy of linear ADS models with the Iterated Function Systems known in chaos theory, we derive the Frobenius-Perron equation for the stationary damage density distribution and analytically compute the damage distribution moments and fractal dimensions. We also investigate nonlinear and stochastic variants of ADS models and show the robustness of the salient features of the damage distributions. |
Wednesday, March 8, 2023 12:18PM - 12:30PM |
N01.00003: Background-dependent sensory bet-hedging in chemotactic bacteria Jeremy Moore, Keita Kamino, Thierry Emonet, Thomas Shimizu Phenotypic diversity allows populations to hedge their bets when future conditions are uncertain. But how should populations distribute phenotypes when they are certain about some environmental cues but not others? Previous studies measuring cell-to-cell variability in sensitivity to attractants revealed that the chemotaxis network of Escherichia coli, can switch between a high diversity bet-hedging regime, and a low diversity tracking regime for a signal as that signal becomes prevalent. Here, we combine mathematical modeling and single-cell FRET experiments to show that populations of chemotactic bacteria make this transition for each ligand independently. That is, transitioning to tracking one ligand does not compromise the population’s ability to hedge its bets across other future ligands. Remarkably, we found that populations maintain this bet-hedging capability even if the background and foreground ligands compete for receptor binding sites. We explain the independence of the transition between two diversity regimes in chemotaxis with a simple allosteric model of chemoreceptor clusters with precise adaptation. We then extend our model to show that the same effects of background stimuli on diversity arise in simple feed-forward signaling architectures in the perfectly-adapting regime. Our findings shed light on general properties of signaling architecture that allows populations to independently modulate the degree of phenotypic diversity in response to environmental conditions. |
Wednesday, March 8, 2023 12:30PM - 12:42PM |
N01.00004: Physical limits on size precision in single-celled microorganisms Daniel McCusker, David K Lubensky Cells divide at a reproducible final size, even though growth and signaling dynamics are noisy. Experiments have shown that unicellular organisms' division size typically varies by about 10% in a constant environment. To investigate the origins of this precision, we study the fundamental physical limits on setting a size in single cells. We model stochastic growth dynamics and use a first-passage formalism wherein the cell decides to stop growing based on an internal, noisy estimate of its size. When growth and measurement noise are white and uncorrelated, we find that a Kalman filter which minimizes the dynamical estimator error also approximately minimizes the division size variance. We analyze published data and find that E. coli displays long correlation times in its growth rate, while S. pombe displays short correlation times. For S. pombe, simple estimates of white noise in estimator dynamics place our model prediction below the experimental 10% level, suggesting that long correlation times in the estimator noise limit S. pombe's ability to measure its size. In light of this finding, we discuss the relationship between an optimal dynamical filter corrupted by correlated measurement noise and the size variance predicted by our first-passage formalism. |
Wednesday, March 8, 2023 12:42PM - 12:54PM |
N01.00005: Kinship Effects in Stochastic Antibiotic Killing of Bacteria. Wesley Stine, Tats Akiyama, Minsu Kim Antibiotic susceptibility is a complex trait that varies from cell to cell, even when the cells are genetically identical. Our previous research indicated that antibiotic killing is in large part stochastic. Here, we directly quantified the degree of stochastic killing. We grew Escherichia coli cells in media containing a b-lactam drug, cefsulodin. We analyzed the growth and death of individual cells and turned the dataset into a genealogical tree, precisely identifying the relationship between bacterial cells. We then calculated pairwise correlation in the survival rates of related cells, starting with siblings, and moving on to more distant relatives such as aunt/niece, first cousins, and so forth. We found a statistically significant correlation for the survival of siblings, but none for more distant relationships. We also found that cells which inherit a pole that is several generations old have a survival advantage over cells with new poles. |
Wednesday, March 8, 2023 12:54PM - 1:06PM |
N01.00006: Precise Spatial Scaling in the Early Fly Embryo Milos Nikolic, William S Bialek, Thomas Gregor The fruit fly embryo is a particularly well-suited system for studying the problem of biological pattern scaling. Embryos from a single inbred strain vary in length by 4%, yet structural markers such as the cephalic furrow are positioned with ~1% accuracy in scaled coordinates. The body plan along the anterior axis of the embryo emerges from a cascaded gene regulatory network: maternal inputs drive mutually interacting gap genes which in turn drive striped patterns of expression in pair rule genes. Previous work has shown that positions of the pair rule stripes, which are distributed along the length of the embryo, scale to embryo length with 1% precision or better. Here we show that boundaries of gap gene expression also scale, and more strongly that local gene expression levels provide information about relative position but no additional information about absolute position or embryo length. In contrast, we do not see scaling of the maternal inputs. These results point toward models in which the gap gene network has a near-degenerate manifold of solutions that can be "pinned" by the maternal inputs. |
Wednesday, March 8, 2023 1:06PM - 1:18PM |
N01.00007: Investigating the importance of migration and spatial heterogeneity in emerging spatial patterns of host-parasitoid populations Appilineni Kushal
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Wednesday, March 8, 2023 1:18PM - 1:30PM |
N01.00008: Variability in responses to random perturbations constrain biomolecular network interactions Seshu R Iyengar, Andreas Hilfinger Perturbation experiments are a common technique to identify interactions in biochemical reaction networks; the response of the system of interest is observed after it is exposed to a range of drugs or disruptions. Average responses of cellular components are then analysed to infer the strengths of connections in the network. Previous approaches like Modular Response Analysis require specific knowledge of each perturbation's targets in the network and only analyse deterministic averages. We demonstrate that the correlation and variation of molecular responses can constrain the interaction topology between two connected components of interest, even when perturbation targets are unknown. In a model system we illustrate how this approach can distinguish between feedback through the other measured component and feedback that is independent of the other component. Finally, we quantify how well our approach—based on deterministic interactions and infinitesimal perturbations—describes biological systems with non-linear stochastic interactions and finite perturbations. This work can help analyse large-scale drug perturbation studies where the exact nature of each perturbation is unknown. |
Wednesday, March 8, 2023 1:30PM - 1:42PM |
N01.00009: Examining variation in gene expression and co-expression using single-cell genomics and method of moments estimators Min Cheol Kim Differential expression analysis of scRNA-seq data is central for characterizing how experimental factors affect the distribution of gene expression. However, it remains challenging to distinguish biological and technical sources of cell-cell variability and to assess the statistical significance of quantitative comparisons between groups of cells. We introduce memento to address these limitations and enable accurate and efficient differential expression analysis of the mean, variability, and gene correlation from scRNA-seq. We used memento to analyze 70,000 tracheal epithelial cells to identify interferon response genes with distinct variability and correlation patterns, 160,000 T cells perturbed with CRISPR-Cas9 to reconstruct gene-regulatory networks that control T cell activation, and 1.2 million PMBCs to map cell-type-specific cis expression quantitative trait loci (eQTLs). In all cases, memento identified more significant and reproducible differences in mean expression but also identified differences in variability and gene correlation that suggest distinct modes of transcriptional regulation imparted by cytokines, genetic perturbations, and natural genetic variation. These results demonstrate memento as a first-in-class method for the quantitative comparisons of scRNA-seq data scalable to millions of cells and thousands of samples. |
Wednesday, March 8, 2023 1:42PM - 1:54PM |
N01.00010: Exploiting fluctuations in gene expression to infer causal interactions between genes Euan Joly-Smith, Paige Allard, Fotini Papazotos, Laurent Potvin-Trottier, Andreas Hilfinger Characterizing gene regulatory networks to gain a mechanistic description of cellular behaviours is a key challenge in systems biology. Identifying causal interactions between genes is traditionally done with perturbation experiments. However, precise perturbation experiments with known targets are not always possible while keeping cells in their physiologically relevant regime. We present a novel approach to use naturally occurring stochastic fluctuations in cells to infer causal interactions between genes, without the need to perturb cells or follow cells over time. Our approach exploits the fact that gene expression noise propagates through causally connected genes. This noise propagation can be identified through violations of a co-variability condition using a passive reporter. Our theoretical results promise to harness the information contained in cell heterogeneity data obtained from single-cell sequencing and flow cytometry experiments. We thus report experimental data to quantitatively test our theoretical results, using well-characterized synthetic gene regulatory circuits in E. coli. |
Wednesday, March 8, 2023 1:54PM - 2:06PM |
N01.00011: Probing the role of “power strokes” in a molecular motor with nonequilibrium simulations Emanuele Penocchio, Geyao Gu, Alex T Albaugh, Todd Gingrich
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Wednesday, March 8, 2023 2:06PM - 2:18PM |
N01.00012: Controlling molecular heat conduction using temperature modulations RENAI CHEN, Tammie Gibson, Galen Craven Over the past decade, there has been growing interest in the field of nanoscale heat transfer, particularly in context of molecular heat conduction. Recently, this interest has sharply increased due to experiments that have measured molecular thermal conductance at the single-molecule level. The broad class of molecular structures that can be realized through advanced molecular synthesis techniques in inorganic, organic, and material chemistry provide possibilities for fabricating molecular structures that could be used to advance the field phononics, that is, using phononic heat transfer to perform complex logic operations. To control these phononic molecular devices, we propose manipulating the heat current through the device by modulating its temperature. Specifically, we explore how modifying thermal transport properties by periodically modulating temperatures of thermal reservoirs in contact with the device can be used to control heat currents at the molecular level. Our theoretical framework includes Langevin dynamics and Nonequilibrium Green's function approaches. Using stochastic molecular dynamics simulations, we confirm the validity of our theoretical approach for examining thermal transport in driven systems. Our results indicate that controlling molecular heat currents using time-periodic temperature modulations could be used to advance the design of molecule-based thermal devices. |
Wednesday, March 8, 2023 2:18PM - 2:30PM |
N01.00013: Evolutionary dynamics of homeostasis mechanisms Edo Kussell, Spencer Hobson-Gutierrez Homeostasis mechanisms exist in many different forms across biological systems. What are the basic principles that govern their evolution? This talk examines this question using simulations and theory. We analyze simple models of homeostasis that maintain trait values within specific ranges, and determine when such mechanisms are evolutionarily advantageous. Potential applications to experiments will be discussed. |
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